# j2kun/mlir-tutorial

MLIR For Beginners tutorial

Repository: https://github.com/j2kun/mlir-tutorial
Canonical: https://ross.abutalabs.com/products/mlir-tutorial
Language: C++
License Family: other
Topics: compilers, mlir, tutorial
Last push: 2025-07-18T17:08:40+00:00

## Health v2 (maintenance only)
Score: 45/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 32, release rhythm 35, longevity 91
- inputs: {"age_days": 1286, "days_push": 411, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1347, forks 142 (observed 2026-08-28T04:04:27.551810+00:00)

## What it is
A tutorial series and accompanying code repository teaching the MLIR compiler framework for building compilers. It walks through defining dialects, writing passes, lowering, and optimization with Bazel and CMake builds.

## Use cases
- learn MLIR from scratch
- understand how to write compiler passes in MLIR
- define a custom MLIR dialect
- lower MLIR code through LLVM
- learn tablegen for compiler passes
- study dataflow analysis in MLIR

## When to choose
- you are a beginner wanting a structured, article-driven introduction to MLIR
- you want runnable example code accompanying each compiler concept
- you need to learn dialect definition, passes, and lowering step by step

## When to avoid
- you need production compiler infrastructure rather than educational examples
- you want a comprehensive MLIR reference covering all advanced features
- you need a project with an explicit open-source license for reuse

## Facets
- artifact type: learning-resource
- maturity: active
- function: compiler, developer-tools
- domain: compilers, tutorials, programming-languages
- platform: cpp
- tags: mlir, llvm, compiler-infrastructure, bazel, cmake, dialects, compiler-passes, tablegen, linux, macos

## Member repositories
- j2kun/mlir-tutorial (main) score 45

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:04:27.551810+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T04:42:36.885163+00:00, confidence not recorded.
  - readme: https://github.com/j2kun/mlir-tutorial (fetched 2026-08-28T04:04:27.551810+00:00, sha 5eb888e32198)
- Data as of 2026-08-30T08:39:29.467469+00:00.